Last week, Reed Wilen, an elite gamer who uses the handle "Chicago" in Rocket League, a popular vehicular-soccer game, encountered a strange and troubling new opponent. From a report: The player seemed like a novice at first, moving their rocket-powered vehicle in a hesitant and awkward way. Then they caught and balanced the ball perfectly on the hood of their car, and dribbled it with superhuman skill towards the goal at high speed. Not only was the other driver clearly a bot -- it was also ridiculously good. "It is very confusing to play against," Wilen says. "Its perfect dribbling would cause havoc on almost every player." Wilen is one of a number of elite Rocket League players to have recently encountered the bot in competitive play. It is not yet good enough to beat all comers, but it can play to a high level, allowing less skilled players to cheat their way to a higher ranking.

Rocket League is frenetic and extremely tricky to play. Each player controls a car capable of impossible acrobatics inside an arena where gravity and physics are apparently set to ludicrous mode. The objective is to use your vehicle to maneuver a giant ball past your opponent and into their goal, a task that requires considerable skill and patience. Sometimes two players work together as a team, making huge leaps, desperate parries, and accidentally colliding, all while trying to anticipate and counter their opponents' own antics. Top Rocket League players will often launch their cars through the air to move the ball toward the goal, but Wilen says the bot he faced appears to have been trained specifically to carry it on the ground. "The bot doesn't really flip around too often and doesn't jump in the air," he says, apparently because it hasn't been programmed to, or learned how to do so. "Instead, it waits for the ball to come down, where it catches it on top of the car and performs a perfect dribble towards the opposing team's net," Wilen says.

The bot that Wilen and others have come up against is called Nexto. It picked up the ability to dribble and score using an artificial intelligence approach known as reinforcement learning, which has underpinned research breakthroughs that let computers master other difficult games such as Go and Starcraft. The technique has also been applied to more practical areas, including chip design and data center cooling in recent years. Reinforcement learning entails creating a program that can perform a task at a basic level and improve by responding to feedback as it practices. The company behind Rocket League, Psyonix, part of Epic Games, allows players to deploy bots to practice against. In 2020 it made an application programming interface (API) available to help developers build bots more easily. Last April, a group of Rocket League enthusiasts with coding skills announced RLGym, an open source library for building reinforcement-learning bots for Rocket League. Later in the year, the group released several open source AI bots -- including an especially skilled dribbler called Nexto.

theodp writes: On Tuesday, the Micro:bit Educational Foundation, a UK-based education non-profit "on a mission to inspire all children to achieve their best digital future," announced a partnership with US-based and tech giant-backed nonprofit Code.org to offer teachers computing resources to complement use of the handheld BBC micro:bit physical computing device as an extension to the Code.org CS Fundamentals curriculum, which is aimed at introducing Computer Science to children in Kindergarten-5th Grade.

"Physical computing is a great way to engage students in computer science, and I'm excited that Code.org is expanding its offerings in this maker education space," said Code.org CEO Hadi Partovi. "We're delighted to partner with micro:bit to provide physical computing extensions to our existing courses." Micro:bit Educational Foundation CEO Gareth Stockdale added, "Growing a diverse pipeline of tech talent who contribute to the creation of better technology in the world begins in the classroom. We are invested in excellence in computer science education for younger students and are excited by the size of the impact we can create together with Code.org to bring the benefits of physical computing to young learners."

Back in 2015, Microsoft -- a Founding Partner of both the Micro:bit Educational Foundation and Code.org -- partnered with the BBC to provide an estimated 1 million free BBC micro:bits to every 11 or 12 year old in the UK. "The chance to influence the lives of a million children does not come often," Microsoft Research wrote in a 2016 paper explaining the efforts to get the micro:bit into the hands of UK schoolchildren and make it part of the CS curriculum. The paper also cited Code.org and the UK's Computing at School (a Micro:bit Educational Foundation partner that was "born at Microsoft Research Cambridge") as "two significant success at the coding level" of "scaling out an initiative to influence an entire country of students, or even globally."

InfoWorld reports: JavaScript, Java, and Python skills are most in-demand by recruiters, according to a report published this week by tech hiring platforms CodinGame and CoderPad. But while the supply of those skills exceeds demand, the demand for TypeScript, Swift, Scala, Kotlin, and Go skills all exceed supply.

The State of Tech Hiring in 2023, a CodinGame-CoderPad report published January 10, draws on a survey of 14,000 professionals and offers insights into what 2023 may hold for tech industry recruiters and job seekers. The demand for JavaScript, Java, and Python skills is consistent with previous years, the report notes.

Among development frameworks, Node.js, React, and .NET Core proved to be the best-known and most in-demand.
InfoWorld summarizes some other interesting findings:

• "59% of developers do not have a university degree in computer science. Nearly one-third consider themselves primarily self-taught."

• "Developers' main challenges at work include unplanned changes to their schedule, unclear direction, and a lack of technical knowledge by team members."

• "Most teams are now hybrid between remote and on-site work. Only 15% work onsite 100% of the time."

"Can editing code feel more tactile, like painting with Photoshop brushes?"

Researchers at GitHub Next asked that question this week — and then supplied the answer. "We added a toolbox of brushes to our Copilot Labs Visual Studio Code extension that can modify your code.... Just select a few lines, choose your brush, and see your code update."

The tool's web page includes interactive before-and-after examples demonstrating:

• Add Types brush
• Fix Bugs brush
• Add Debugging Statements brush
• Make More Readable brush

And last month Microsoft's principle program manager for browser tools shared an animated GIF showing all the brushes in action.

"In the future, we're interested in adding more useful brushes, as well as letting developers store their own custom brushes," adds this week's announcement. "As we explore enhancing developers' workflows with Machine Learning, we're focused on how to empower developers, instead of automating them. This was one of many explorations we have in the works along those lines."

It's ultimately grafting an incredibly easy interface onto "ML-powered code modification", writes Visual Studio Magazine, noting that "The bug-fixing brush, for example can fix a simple typo, changing a variable name from the incorrect 'low' to the correct 'lo'....

"All of the above brushes and a few others have been added to the Copilot Labs brushes toolbox, which is available for anyone with a GitHub Copilot license, costing $10 per month or $100 per year.... At the time of this writing, the extension has been installed 131,369 times, earning a perfect 5.0 rating from six reviewers."

DirecTV is laying off hundreds of employees -- roughly 10% of its upper ranks -- as the company looks to reduce costs amid the heightened pain of cord cutting for pay-TV providers, according to people familiar with the matter. CNBC reports: Most of the job cuts will be at the manager level, the people said, citing an email to employees sent on Friday. Managers make up about half of DirecTV's fewer than 10,000 employees, one of the people said. The affected employees' last day will be Jan. 20. "The entire pay-TV industry is impacted by the secular decline and the increasing rates to secure and distribute programming," a DirecTV spokesperson said in a statement. "We're adjusting our operations costs to align with these changes and will continue to invest in new entertainment products and service enhancements."

DirecTV and its peers have long been under pressure as customers cut the cord and opt for streaming services. The rate of cord cutting accelerated in the third quarter, according to MoffettNathanson. [...] DirecTV reportedly lost around 500,000 customers in its most recent quarter, according to ratings agency Fitch. Although DirecTV's losses slowed during the height of the pandemic, they recently accelerated to nearly 17%, according to MoffettNathanson.

An anonymous reader quotes a report from MIT Technology Review: A small biotech company claims it has used a technology called reprogramming to rejuvenate old mice and extend their lives, a result suggesting that one day older people could have their biological clocks turned back with an injection -- literally becoming younger. The life-extension claim in rodents, made by Rejuvenate Bio, a San Diego biotech company, appears in a preprint paper on the website BioRxiv and hasn't been peer reviewed.

Noah Davidsohn, chief scientific officer of Rejuvenate, says the company used gene therapy to add three powerful reprogramming genes to the bodies of mice that were equivalent in age to human 77-year-olds. After the treatment, their remaining life span was doubled, the company says. Treated mice lived another 18 weeks, on average, while control mice died in nine weeks. Overall, the treated mice lived about 7% longer. Although the increase in lifespan was modest, the company says the research provides a demonstration of age reversal in an animal. "This is a powerful technology, and here is the proof of concept," says Davidsohn. "I wanted to show that it's actually something we can do in our elderly population."

Scientists not connected to the company called the study an exciting landmark but cautioned that whole-body rejuvenation using gene therapy remains a poorly understood concept with huge risks. "It's a beautiful intellectual exercise, but I would shy away from doing anything remotely similar to a person," says Vittorio Sebastiano, a professor at Stanford University. One risk is that the powerful programming process can cause cancer. Such an effect is often seen in mice. Even so, the chance that reprogramming could be an elixir of youth has led to a research and investment boom. One company, Altos Labs, says it has raised over $3 billion. "Far more information will be needed to learn exactly what changes the reprogramming genes cause in the mice, and researchers say other groups will need to repeat the experiment before they are convinced," adds the report.

"Sebastiano says the life-extension effect reported by Rejuvenate could be due to changes in a single organ or group of cells, rather than a general mouse-wide rejuvenation effect. Among other shortfalls in its research, Rejuvenate did not carefully document which and how many cells were changed by the genetic treatment."

"The Tiobe index gauges language popularity using a formula that assesses searches on programming languages in Google, Bing, Yahoo, Wikipedia, and other search engines," writes InfoWorld. And they add that this year the "vaunted" C++ programming language was the index's biggest gainer in 2022.

TIOBE's announcement includes their calculation that C++ rose 4.62% in popularity in 2022: Runners up are C (+3.82%) and Python (+2.78%). Interestingly, C++ surpassed Java to become the number 3 of the TIOBE index in November 2022. The reason for C++'s popularity is its excellent performance while being a high level object-oriented language. Because of this, it is possible to develop fast and vast software systems (over millions of lines of code) in C++ without necessarily ending up in a maintenance nightmare.
So which programming languages are most popular now? For what it's worth, here's TIOBE's latest ranking:


- Python
- C
- C++
- Java
- C#
- Visual Basic
- JavaScript
- SQL
- Assembly Language
- PHP


InfoWorld adds that "Helping C++ popularity was the publication of new language standards with interesting features, such as C++ 11 and C++ 20."

More from TIOBE: What else happened in 2022? Performance seemed to be important. C++ competitor Rust entered the top 20 again (being at position #26 one year ago), but this time it seems to be for real. Lua, which is known for its easy interfacing with C, jumped from position #30 to #24. F# is another language that made an interesting move: from position #74 to position #33 in one years' time. Promising languages such as Kotlin (from #29 to #25), Julia (from #28 to #29) and Dart (from #37 to #38) still have a long way to go before they reach the top 20. Let's see what happens in 2023.

January's Communications of the ACM includes an essay predicting "the end of programming," in an AI-powered future where "programming will be obsolete."

But IT analyst and ZDNet contributor Joe McKendrick remains skeptical, judging by a new essay sardonically titled "It's the end of programming as we know it — again." Over the past few decades, various movements, paradigms, or technology surges — whatever you want to call them — have roiled the software world, promising either to hand a lot of programming grunt work to end users, or automate more of the process. CASE tools, 4GL, object-oriented programming, service oriented architecture, microservices, cloud services, Platform as a Service, serverless computing, low-code, and no-code all have theoretically taken the onerous burdens out of software development. And, potentially, threaten the job security of developers.

Yet, here we are. Software developers are busier than ever, with demand for skills only increasing.

"I remember when the cloud first started becoming popular and companies were migrating to Office 365, everyone was saying that IT Pros will soon have no job," says Vlad Catrinescu, author at Pluralsight. "Guess what — we're still here and busier than ever."

The question is how developers' job will ultimately evolve. There is the possibility that artificial intelligence, applied to application development and maintenance, may finally make low-level coding a thing of the past.... Catrinescu believes that the emerging generation of automated or low-code development solutions actually "empowers IT professionals and developers to work on more challenging applications. IT departments can focus on enterprise applications and building complicated apps and automations that will add a lot of value to the enterprise."
Even the man predicting "the end of programming" in an AI-powered future also envisions new technology that "potentially opens up computing to almost anyone" (in ACM's video interview). But in ZDNet's article Jared Ficklin, chief creative technologist and co-founder of argodesign, even predicts the possibility of real-time computing.

"You could imagine asking Alexa to make you an app to help organize your kitchen. AI would recognize the features, pick the correct patterns and in real time, over the air deliver an application to your mobile phone or maybe into your wearable mobile computer."

Matt Welsh is the CEO and co-founder of Fixie.ai, an AI-powered software delivery company founded by a team from Google and Apple. "I believe the conventional idea of 'writing a program' is headed for extinction," he opines in January's Communications of the ACM, "and indeed, for all but very specialized applications, most software, as we know it, will be replaced by AI systems that are trained rather than programmed."

His essay is titled "The End of programming," and predicts a future will "Programming will be obsolete." In situations where one needs a "simple" program (after all, not everything should require a model of hundreds of billions of parameters running on a cluster of GPUs), those programs will, themselves, be generated by an AI rather than coded by hand.... with humans relegated to, at best, a supervisory role.... I am not just talking about things like Github's CoPilot replacing programmers. I am talking about replacing the entire concept of writing programs with training models. In the future, CS students are not going to need to learn such mundane skills as how to add a node to a binary tree or code in C++. That kind of education will be antiquated, like teaching engineering students how to use a slide rule.

The engineers of the future will, in a few keystrokes, fire up an instance of a four-quintillion-parameter model that already encodes the full extent of human knowledge (and then some), ready to be given any task required of the machine. The bulk of the intellectual work of getting the machine to do what one wants will be about coming up with the right examples, the right training data, and the right ways to evaluate the training process. Suitably powerful models capable of generalizing via few-shot learning will require only a few good examples of the task to be performed. Massive, human-curated datasets will no longer be necessary in most cases, and most people "training" an AI model will not be running gradient descent loops in PyTorch, or anything like it. They will be teaching by example, and the machine will do the rest.

In this new computer science — if we even call it computer science at all — the machines will be so powerful and already know how to do so many things that the field will look like less of an engineering endeavor and more of an an educational one; that is, how to best educate the machine, not unlike the science of how to best educate children in school. Unlike (human) children, though, these AI systems will be flying our airplanes, running our power grids, and possibly even governing entire countries. I would argue that the vast majority of Classical CS becomes irrelevant when our focus turns to teaching intelligent machines rather than directly programming them. Programming, in the conventional sense, will in fact be dead....

We are rapidly moving toward a world where the fundamental building blocks of computation are temperamental, mysterious, adaptive agents.... This shift in the underlying definition of computing presents a huge opportunity, and plenty of huge risks. Yet I think it is time to accept that this is a very likely future, and evolve our thinking accordingly, rather than just sit here waiting for the meteor to hit.
"I think the debate right now is primarily around the extent to which these AI models are going to revolutionize the field," Welsh says in a video interview. "It's more a question of degree rather than whether it's going to happen....

"I think we're going to change from a world in which people are primarily writing programs by hand to a world in which we're teaching AI models how to do things that we want them to do... It starts to feel more like a field that focuses on AI education and maybe even AI psychiatry. In order to solve these problems, you can't just assume that people are going to be writing the code by hand."

Remember when MIT researchers did fMRI brain scans measuring the blood flow through brains to determine which parts were engaged when programmers evaluated code? MIT now says that a new paper (by many of the same authors) delves even deeper: Whereas the previous study looked at 20 to 30 people to determine which brain systems, on average, are relied upon to comprehend code, the new research looks at the brain activity of individual programmers as they process specific elements of a computer program. Suppose, for instance, that there's a one-line piece of code that involves word manipulation and a separate piece of code that entails a mathematical operation. "Can I go from the activity we see in the brains, the actual brain signals, to try to reverse-engineer and figure out what, specifically, the programmer was looking at?" asks Shashank Srikant, a PhD student in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "This would reveal what information pertaining to programs is uniquely encoded in our brains." To neuroscientists, he notes, a physical property is considered "encoded" if they can infer that property by looking at someone's brain signals.

Take, for instance, a loop — an instruction within a program to repeat a specific operation until the desired result is achieved — or a branch, a different type of programming instruction than can cause the computer to switch from one operation to another. Based on the patterns of brain activity that were observed, the group could tell whether someone was evaluating a piece of code involving a loop or a branch. The researchers could also tell whether the code related to words or mathematical symbols, and whether someone was reading actual code or merely a written description of that code.....

The team carried out a second set of experiments, which incorporated machine learning models called neural networks that were specifically trained on computer programs. These models have been successful, in recent years, in helping programmers complete pieces of code. What the group wanted to find out was whether the brain signals seen in their study when participants were examining pieces of code resembled the patterns of activation observed when neural networks analyzed the same piece of code. And the answer they arrived at was a qualified yes. "If you put a piece of code into the neural network, it produces a list of numbers that tells you, in some way, what the program is all about," Srikant says. Brain scans of people studying computer programs similarly produce a list of numbers. When a program is dominated by branching, for example, "you see a distinct pattern of brain activity," he adds, "and you see a similar pattern when the machine learning model tries to understand that same snippet."
But where will it all lead? They don't yet know what these recently-gleaned insights can tell us about how people carry out more elaborate plans in the real world.... Creating models of code composition, says O'Reilly, a principal research scientist at CSAIL, "is beyond our grasp at the moment." Lipkin, a BCS PhD student, considers this the next logical step — figuring out how to "combine simple operations to build complex programs and use those strategies to effectively address general reasoning tasks." He further believes that some of the progress toward that goal achieved by the team so far owes to its interdisciplinary makeup. "We were able to draw from individual experiences with program analysis and neural signal processing, as well as combined work on machine learning and natural language processing," Lipkin says. "These types of collaborations are becoming increasingly common as neuro- and computer scientists join forces on the quest towards understanding and building general intelligence."

According to the Information, Amazon is working on a standalone app for watching sports content. Reuters reports: Sports remains one of the biggest attractions for live viewing as U.S. audiences increasingly switch from pay TV subscriptions to streaming apps. The move will likely complement Amazon's effort to double down on sports programming through its Prime Video service, a key channel to attract consumers to its shopping platform.

Amazon already owns the rights to stream games including National Football League's Thursday Night Football franchise and Premier League soccer matches in the UK, setting it up to better compete with sports streaming leader Walt Disney Co. It was not clear when Amazon would roll out the sports app and whether it would go through with the plan, according to the report.

An anonymous reader quotes a report from TechCrunch: A recent study finds that software engineers who use code-generating AI systems are more likely to cause security vulnerabilities in the apps they develop. The paper, co-authored by a team of researchers affiliated with Stanford, highlights the potential pitfalls of code-generating systems as vendors like GitHub start marketing them in earnest. The Stanford study looked specifically at Codex, the AI code-generating system developed by San Francisco-based research lab OpenAI. (Codex powers Copilot.) The researchers recruited 47 developers -- ranging from undergraduate students to industry professionals with decades of programming experience -- to use Codex to complete security-related problems across programming languages including Python, JavaScript and C.

Codex was trained on billions of lines of public code to suggest additional lines of code and functions given the context of existing code. The system surfaces a programming approach or solution in response to a description of what a developer wants to accomplish (e.g. "Say hello world"), drawing on both its knowledge base and the current context. According to the researchers, the study participants who had access to Codex were more likely to write incorrect and "insecure" (in the cybersecurity sense) solutions to programming problems compared to a control group. Even more concerningly, they were more likely to say that their insecure answers were secure compared to the people in the control.

Megha Srivastava, a postgraduate student at Stanford and the second co-author on the study, stressed that the findings aren't a complete condemnation of Codex and other code-generating systems. The study participants didn't have security expertise that might've enabled them to better spot code vulnerabilities, for one. That aside, Srivastava believes that code-generating systems are reliably helpful for tasks that aren't high risk, like exploratory research code, and could with fine-tuning improve in their coding suggestions. "Companies that develop their own [systems], perhaps further trained on their in-house source code, may be better off as the model may be encouraged to generate outputs more in-line with their coding and security practices," Srivastava said. The co-authors suggest vendors use a mechanism to "refine" users' prompts to be more secure -- "akin to a supervisor looking over and revising rough drafts of code," reports TechCrunch. "They also suggest that developers of cryptography libraries ensure their default settings are secure, as code-generating systems tend to stick to default values that aren't always free of exploits."

Computer scientists from Stanford University have found that programmers who accept help from AI tools like Github Copilot produce less secure code than those who fly solo. From a report: In a paper titled, "Do Users Write More Insecure Code with AI Assistants?", Stanford boffins Neil Perry, Megha Srivastava, Deepak Kumar, and Dan Boneh answer that question in the affirmative. Worse still, they found that AI help tends to delude developers about the quality of their output. "We found that participants with access to an AI assistant often produced more security vulnerabilities than those without access, with particularly significant results for string encryption and SQL injection," the authors state in their paper.

"Surprisingly, we also found that participants provided access to an AI assistant were more likely to believe that they wrote secure code than those without access to the AI assistant." Previously, NYU researchers have shown that AI-based programming suggestions are often insecure in experiments under different conditions. The Stanford authors point to an August 2021 research paper titled "Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions," which found that given 89 scenarios, about 40 per cent of the computer programs made with the help of Copilot had potentially exploitable vulnerabilities.

That study, the Stanford authors say, is limited in scope because it only considers a constrained set of prompts corresponding to 25 vulnerabilities and just three programming languages: Python, C, and Verilog. The Stanford scholars also cite a followup study from some of the same NYU eggheads, "Security Implications of Large Language Model Code Assistants: A User Study," as the only comparable user study they're aware of. They observe, however, that their work differs because it focuses on OpenAI's codex-davinci-002 model rather than OpenAI's less powerful codex-cushman-001 model, both of which play a role in GitHub Copilot, itself a fine-tuned descendant of a GPT-3 language model.

Like a visit from an old friend, it's Donald Knuth's annual Christmas tree lecture for 2022. "Because of the pandemic, it's been three years since Knuth has been able to honor this tradition," notes The New Stack: 2022 marks the 60th anniversary of that fateful day in 1962 when a 24-year-old Donald Knuth started writing " The Art of Computer Programming." Now approaching his 85th birthday, Knuth has become almost a legend in the world of computer programming — and he's still writing additional volumes for his massive analysis of algorithms. But every year, right around Christmas time, there's another tradition. Knuth gives a special lecture "pitched at non-specialists" for a small audience at Stanford University (where Knuth is a professor emeritus) and a larger audience online...

Hunched over a notepad (which was projected onto a screen behind him), Knuth began the 26th annual Christmas lecture by pointing out that the evening's topic had been hiding in plain sight for two decades. For the first 20 years, they'd called them the "Christmas tree" lectures, since "trees are one of the most important things to a computer scientist. And every year I learned at least two new cool things about trees..."

About five years ago they'd changed the name to just "Christmas lectures" — but the problem wasn't that trees stopped being interesting. "I still learn cool things about trees every year. But they're getting harder and harder to explain to a general audience!"

So this year's triumphant "homecoming" lecture would indeed include trees — specifically a phenomenon Knuth describes as "twintrees," along with Baxter permutations, and Floorplans. Knuth noted they're all topics touched on in the latest volume of The Art of Computer Programming, before jokingly reminding the audience that his book makes an excellent Christmas present.
By the end of the lecture, Knuth had written algorithms for all three mathematical concepts — then connected all three algorithms with Linux pipes to show what happens when you convert one kind of sequence into the other and then into the other.

"I get back, of course, the one I started with!"

A Valero gas station sells approximately 5,000 gallons of gas a day, one employee estimates.

But local police arrested six men who, in a series of robberies, tricked the pumps out of 30,000 gallons of gasoline, reports the Mercury News, "a haul authorities estimated was worth at least $180,000." Upon further inspection of surveillance video, authorities said, police saw one of the suspects activate a gas-pump computer, allowing another suspect to pump fuel into his vehicle.... An employee from the Valero station, who declined to give their name, called the process the gas thieves used "nearly untraceable."

"You must have a deep understanding of how the pump system works," the person said. "There is a time frame anywhere from 75 seconds to two minutes for the authorization to go through the network [after sliding a credit card into a gas pump]. In this (time period), there's an opportunity to manipulate the pump ... You're able to manipulate the pump and confuse the programming to an extent that the pump starts dispensing gas...."

In a Facebook post, authorities said the three suspects had been "conspiring together in a sophisticated operation to thwart security devices and pump electronics to steal large amounts of gasoline from the business...."

Authorities say $20,000 of damage was done to gas pumps.
Thanks to Slashdot reader k6mfw for submitting the story.

For three years Stockholm-based games studio Embark has been working on the Rust-gpu project to make Rust "a first class language and ecosystem for GPU programming." The project's latest announcement? rust-gpu now supports ray-tracing.

Their original announcement explained the rationale for this years-long dvelopment effort: Historically in games GPU programming has been done through writing either HLSL, or to a lesser extent GLSL. These are simple programming languages that have evolved along with rendering APIs over the years. However, as game engines have evolved, these languages have failed to provide mechanisms for dealing with large codebases, and have generally stayed behind the curve compared to other programming languages.

In part this is because it's a niche language for a niche market, and in part this has been because the industry as a whole has sunk quite a lot of time and effort into the status quo. While over-all better alternatives to both languages exist, none of them are in a place to replace HLSL or GLSL. Either because they are vendor locked, or because they don't support the traditional graphics pipeline. Examples of this include CUDA and OpenCL. And while attempts have been made to create language in this space, none of them have gained any notable traction in the gamedev community.

Our hope with this project is that we push the industry forward by bringing an existing, low-level, safe, and high performance language to the GPU; namely Rust. And with it come some additional benefits that can't be overlooked: a package/module system that's one of the industry's best, built in safety against race-conditions or out of bounds memory access, a wide range of tools and utilities to improve programmer workflows, and many others!
Along with ray-tracing, this week they announced plans to keep rust-gpu on the same schedule as the stable Rust release, "so you can use your favorite new language features as new stable versions of Rust are being released, by just updating your rust-gpu version."

Thanks to Slashdot reader guest reader for sharing the news!

A Microsoft .NET Community standup has left Windows desktop developers wondering what kind of future, if any, the company has planned for its older desktop application frameworks, Windows Forms and Windows Presentation Foundation (WPF). From a report: A "what's new" slide for WPF presented by senior program manager Olia Gavrysh last week shows "Community Run Project" as the first bullet point, causing consternation among attendees. "Who's happy that WPF is now a community run project? This is soooo scary," remarked Morten Nielsen, a senior principal engineer at ESRI working on the ArcGIS runtime, for location-based analytics.

The slide was perhaps misinterpreted. It was intended as an update on what is happening with pull requests from the community, rather than meaing that WPF has been handed over to the community. Nevertheless, concerns about the future of the framework are well founded. "It's not dead. we have a team working on WPF and supporting it," said Gavrysh, but added, "we now switch to the model where we accept a lot of PRs [pull requests] from the community because we think of WPF as [a] very mature project so not that much rapid development is happening."

Confidential data of about 112,000 taxpayers inadvertently published by the IRS over the summer was mistakenly republished in late November and remained online until early December, the IRS disclosed last week. From a report: Form 990-T data that was supposed to stay private had been taken offline but made its way back to the IRS site when a contractor uploaded an old file that still included most of the private information, a letter sent Thursday to congressional leaders said. The agency is required to make Form 990-Ts filed by nonprofit groups available online but is supposed to keep the form filed by individuals private; in both cases, the agency made that information available too.

An internal programming error caused the September release of private forms along with the ones filed by nonprofit groups, the letter said. This time, the contractor tasked with managing the database reuploaded the older file with the original data instead of a new file that filtered out the forms that needed to be kept private. The IRS shared corrected data with the contractor on Nov. 23, but the old files had not been purged from their system. A third-party researcher alerted the IRS the files were back online on Dec. 1, and the IRS ordered the contractor to take them down immediately. Roughly 104,000 of the 106,000 forms disclosed in September were redisclosed this time. The agency is reconsidering its relationship with the contractor Accenture on this project, the report added, citing a person familiar with the matter.

"Peak TV has peaked," reports the new York Times: The never-ending supply of new programming that helped define the streaming era — spawning shows at a breakneck pace but also overwhelming viewers with too many choices — appears to finally be slowing. The number of adult scripted series ordered by TV networks and streaming companies aimed for U.S. audiences fell by 24 percent in the second half of this year, compared with the same period last year, according to Ampere Analysis, a research firm. Compared with 2019, it is a 40 percent drop. "The second half of the year has really gone off a bit of a cliff," said Fred Black, a research manager at Ampere.

It may take some time for that to become apparent to viewers — if it becomes apparent at all, given the glut. It is usually months and sometimes more than a year for a TV show to premiere after a network orders it.

The drop is a result of broader reckoning inside the entertainment industry. For years, television executives tossed off billions of dollars on TV series to help build out their streaming services and chase subscribers. The spending has been a boon to high-profile writers and producers, who captured eight- and nine-figure deals, as well as for the actors, directors and behind-the-scenes workers who kept the engine going. But Wall Street soured on the buy-at-any-cost strategy starting in the spring, when Netflix, the streaming powerhouse, announced that it had lost subscribers for the first time in a decade. Netflix's stock nose-dived, and other entertainment companies soon watched their share prices fall, too. Hollywood companies quickly shifted, putting a new emphasis on higher profits instead of raw subscriber counts.

Then, in recent months, entertainment companies became increasingly anxious about a slowing economy, the cord-cutting movement and a troublesome advertising market. Since the summer, scores of executives have abruptly been dismissed, strict cost-cutting measures have been adopted and layoffs have taken hold throughout the industry.... Netflix also cut hundreds of jobs and introduced a cheaper advertising tier, overturning the company's longtime pledge to never allow commercials on the service. Warner Bros. Discovery, a company that was formed in April, faces a debt of roughly $50 billion, and has been in severe cost-cutting mode. There have been rounds of layoffs companywide, including at HBO and HBO Max, as well as sudden cancellations. The once-popular series "Westworld" was canceled last month — a move that surprised Hollywood — and the lesser-known, raunchy dating series "FBoy Island" was cut a few weeks ago....

There are a few outliers to this year's trend: Apple TV+ and Amazon have increased the number of adult scripted series they have purchased this year. So has Disney, according to Ampere's research. (For the second half of the year, however, Disney's buying has declined compared with the same period last year.)

Stack Overflow has the announced the results of its annual survey of developers. ZDNet reports: Almost three-quarters (74%) of developers are actively looking for new roles or are open to fresh opportunities, according to research.... The highest percentage of active job seekers is in the 20-24 year-old cohort (27%), with 21% for 25-34 year-olds, 17% for 35-44 year-olds, and only 12% for 45-54 year-olds.

Additionally, the percentage of younger developers actively searching for their next role increased nine points year over year, according to the survey of 2,600 developers by StackOverflow....

Some 54% of respondents to the StackOverflow survey said a better salary is the largest motivator when considering a new opportunity. The biggest factors that stop developers from looking for new jobs are flexibility (58%), salary (54%), and learning opportunities (54%). Developers also want flexibility and the option to work from home, with 46% citing starting/ending the day at a precise time or being expected to work from an office (44%) as the top drawbacks in their current roles.

"Regardless of the economy, it's clear salary is important but it's not everything," says StackOverflow CEO Prashanth Chandrasekar.